This series of files compile all analyses done during Chapter 3:
- Section 1 presents the calculation of the indices of exposure.
- Section 2 presents variable exploration and regressions results.
- Section 3 presents species distribution models.
All analyses have been done with R 4.1.0.
Click on the table of contents in the left margin to assess a specific analysis.
Click on a figure to zoom it
⏪ | 🏠 | ⏩
1. Spatial variation of exposure indices
Here, we compute semivariograms for each exposure index (on the whole raster, not only extracted values at the stations).
Aquaculture

Dredging

Runoff

Sewers

Structures

Shipping

Fisheries

2. Relationships with abiotic parameters and biotic indices
Biotic indices have been calculated during Chapter 2.
2.1. Covariation
Several types of models were considered to explore relationships: linear, quadratic, exponential and logarithmic. The model with the highest \(R^{2}\) is presented on each plot.
⚠️ Only linear models are implemented now, as there are some bugs with the automatized calculation of the others.
Aquaculture


Dredging


Runoff


Sewers


Structures


Shipping


Fisheries


Cumulative exposure


2.2. Correlation
Correlations have been calculated with Spearman’s rank coefficient.
Correlation coefficients between exposure and variables/indices
| aquaculture |
-0.088 |
0.065 |
0.023 |
-0.025 |
0.051 |
-0.282 |
-0.24 |
-0.355 |
-0.476 |
-0.483 |
-0.479 |
-0.286 |
-0.267 |
-0.413 |
-0.02 |
-0.055 |
0.158 |
0.152 |
0.209 |
0.223 |
0.191 |
0.157 |
0.262 |
0.204 |
0.18 |
-0.14 |
0.036 |
0.068 |
-0.01 |
-0.192 |
| dredging |
0.238 |
-0.02 |
-0.012 |
0.003 |
0.044 |
0.062 |
0.004 |
0.257 |
0.373 |
0.566 |
0.369 |
-0.025 |
0.126 |
0.28 |
-0.167 |
0.107 |
0.059 |
-0.164 |
-0.05 |
-0.132 |
-0.019 |
0.052 |
-0.027 |
-0.087 |
0.074 |
0.081 |
0.06 |
-0.115 |
-0.015 |
0.155 |
| runoff |
-0.016 |
-0.075 |
0.297 |
-0.194 |
0.018 |
-0.149 |
-0.093 |
0.048 |
0.299 |
0.278 |
0.154 |
-0.126 |
-0.027 |
0.147 |
-0.059 |
-0.068 |
-0.028 |
-0.162 |
-0.065 |
-0.181 |
-0.012 |
0.03 |
-0.085 |
-0.158 |
-0.034 |
0.112 |
0.01 |
0.011 |
-0.004 |
0.031 |
| sewers |
0.424 |
-0.14 |
-0.387 |
0.344 |
0.164 |
0.639 |
0.588 |
0.691 |
0.751 |
0.669 |
0.755 |
0.642 |
0.706 |
0.743 |
-0.091 |
0.053 |
-0.171 |
-0.248 |
-0.23 |
-0.287 |
-0.194 |
-0.108 |
-0.273 |
-0.238 |
-0.187 |
0.176 |
-0.036 |
-0.177 |
0.205 |
0.253 |
| structures |
0.172 |
-0.071 |
0.045 |
-0.006 |
0.076 |
0.052 |
0.044 |
0.277 |
0.465 |
0.514 |
0.404 |
0.057 |
0.165 |
0.326 |
-0.141 |
0.003 |
-0.031 |
-0.219 |
-0.109 |
-0.225 |
-0.054 |
0.012 |
-0.122 |
-0.174 |
-0.035 |
0.047 |
0.039 |
-0.089 |
0.042 |
0.154 |
| shipping |
0.371 |
-0.223 |
-0.228 |
0.238 |
-0.08 |
0.415 |
0.27 |
0.464 |
0.573 |
0.57 |
0.55 |
0.406 |
0.415 |
0.542 |
-0.071 |
0.054 |
-0.003 |
-0.059 |
-0.035 |
-0.059 |
-0.037 |
-0.036 |
-0.082 |
-0.178 |
-0.095 |
0.315 |
-0.045 |
-0.085 |
0.234 |
0.167 |
| fisheries |
-0.492 |
0.202 |
0.376 |
-0.378 |
-0.138 |
-0.567 |
-0.541 |
-0.552 |
-0.606 |
-0.576 |
-0.585 |
-0.54 |
-0.563 |
-0.613 |
0.173 |
-0.066 |
0.097 |
0.309 |
0.224 |
0.28 |
0.167 |
-0.015 |
0.191 |
0.318 |
0.082 |
-0.267 |
0.143 |
0.203 |
-0.226 |
-0.186 |
| cumulative_exposure |
0.277 |
-0.108 |
-0.058 |
0.086 |
0.087 |
0.19 |
0.144 |
0.357 |
0.556 |
0.58 |
0.471 |
0.196 |
0.298 |
0.445 |
-0.092 |
0.03 |
-0.053 |
-0.164 |
-0.107 |
-0.185 |
-0.088 |
-0.048 |
-0.17 |
-0.145 |
-0.075 |
0.215 |
0.033 |
-0.108 |
0.119 |
0.182 |
p-values of correlation test between exposure indices and variables/indices
| aquaculture |
0.3664 |
0.5051 |
0.8152 |
0.7992 |
0.6021 |
0.003082 |
0.01234 |
0.0001626 |
1.949e-07 |
1.228e-07 |
1.579e-07 |
0.002721 |
0.005256 |
8.895e-06 |
0.8385 |
0.5693 |
0.1014 |
0.1157 |
0.02961 |
0.02045 |
0.04805 |
0.1049 |
0.006131 |
0.03454 |
0.06279 |
0.1478 |
0.7139 |
0.4855 |
0.9216 |
0.04691 |
| dredging |
0.01324 |
0.8387 |
0.9003 |
0.9732 |
0.6495 |
0.5259 |
0.9659 |
0.00727 |
6.998e-05 |
1.771e-10 |
8.557e-05 |
0.799 |
0.1938 |
0.003346 |
0.08431 |
0.269 |
0.5434 |
0.09076 |
0.6071 |
0.1748 |
0.8468 |
0.594 |
0.7845 |
0.3704 |
0.446 |
0.4042 |
0.535 |
0.2368 |
0.8796 |
0.1081 |
| runoff |
0.8701 |
0.4395 |
0.001802 |
0.04443 |
0.8515 |
0.1241 |
0.3393 |
0.623 |
0.001656 |
0.003518 |
0.1125 |
0.1942 |
0.7832 |
0.1285 |
0.5414 |
0.4829 |
0.7721 |
0.09401 |
0.5056 |
0.06025 |
0.9 |
0.7592 |
0.38 |
0.1029 |
0.7294 |
0.2487 |
0.9181 |
0.9107 |
0.9663 |
0.7508 |
| sewers |
4.879e-06 |
0.1485 |
3.54e-05 |
0.0002644 |
0.09067 |
9.873e-14 |
2.137e-11 |
1.233e-16 |
7.406e-21 |
2.565e-15 |
3.667e-21 |
6.987e-14 |
1.478e-17 |
3.514e-20 |
0.3471 |
0.5842 |
0.07716 |
0.00972 |
0.01674 |
0.002579 |
0.04455 |
0.2654 |
0.004234 |
0.01294 |
0.05204 |
0.06905 |
0.7087 |
0.06619 |
0.03313 |
0.008293 |
| structures |
0.07492 |
0.4645 |
0.6446 |
0.9475 |
0.4369 |
0.5895 |
0.6483 |
0.003746 |
3.933e-07 |
1.315e-08 |
1.425e-05 |
0.5586 |
0.08784 |
0.0005685 |
0.1453 |
0.9766 |
0.7536 |
0.02293 |
0.2634 |
0.01934 |
0.576 |
0.8993 |
0.2088 |
0.07181 |
0.7189 |
0.6262 |
0.6861 |
0.3619 |
0.6684 |
0.1117 |
| shipping |
7.89e-05 |
0.02058 |
0.01742 |
0.01331 |
0.4089 |
7.945e-06 |
0.004671 |
4.107e-07 |
9.46e-11 |
1.253e-10 |
7.297e-10 |
1.302e-05 |
7.806e-06 |
1.344e-09 |
0.4653 |
0.5813 |
0.9789 |
0.5475 |
0.7225 |
0.5432 |
0.7071 |
0.7092 |
0.3975 |
0.06563 |
0.3268 |
0.0008844 |
0.6448 |
0.3801 |
0.01464 |
0.08464 |
| fisheries |
6.275e-08 |
0.03585 |
6.17e-05 |
5.585e-05 |
0.1551 |
1.607e-10 |
1.476e-09 |
6.146e-10 |
3.727e-12 |
6.679e-11 |
2.989e-11 |
1.623e-09 |
2.366e-10 |
1.852e-12 |
0.07296 |
0.496 |
0.3185 |
0.001128 |
0.01998 |
0.003357 |
0.0847 |
0.8735 |
0.0474 |
0.0007943 |
0.3984 |
0.005238 |
0.1407 |
0.03473 |
0.01884 |
0.05423 |
| cumulative_exposure |
0.003733 |
0.2652 |
0.5492 |
0.3756 |
0.3707 |
0.04849 |
0.1364 |
0.0001492 |
4.022e-10 |
4.899e-11 |
2.628e-07 |
0.04167 |
0.001703 |
1.356e-06 |
0.3421 |
0.7614 |
0.588 |
0.08962 |
0.2702 |
0.05549 |
0.3636 |
0.6214 |
0.07827 |
0.1342 |
0.4407 |
0.02571 |
0.732 |
0.2679 |
0.2197 |
0.05903 |

3. Relationships with benthic communities
3.1. Taxa identity
The most abundant taxa in our study area are:
- Density: B.neotena (1969), E. integra (1158), P.grandimana (1092), Nematoda (1044) and M. calcarea (575)
- Biomass: E. parma (biomass of 531.5), Strongylocentrotus sp. (65.3), N. incisa (58.5), M. calcarea (45.4) and S. groenlandicus (34.3)
The following graphs present the distribution of sampled phyla along index of cumulative exposure, according to density (left side) or biomass (right side).
Exposure categories are based on the exposure index: the higher the index, the lower the status. Five exposure categories (‘bad’, ‘poor’, ‘moderate’, good’, ‘high’) have been set relative to the exposure index (5.6 ≤ \(E\), 4.2 ≤ \(E\) < 5.6, 2.8 ≤ \(E\) < 4.2, 1.4 ≤ \(E\) < 2.8, \(E\) < 1.4, respectively). Maximum detected cumulative exposure is 3.698.
4. Regressions
For the following analyses, independant variables are abiotic parameters and exposure indices, dependant variables are community characteristics. Variables have been standardized by mean and standard-deviation.
4.1. Data manipulation
All stations and predictors were selected for the regressions, as we are interested in each of them (following graphs are for information only).

Correlation coefficients between exposure indices
| aquaculture |
1 |
-0.291 |
-0.536 |
-0.572 |
-0.498 |
-0.485 |
0.456 |
| dredging |
-0.291 |
1 |
0.595 |
0.409 |
0.776 |
0.463 |
-0.28 |
| runoff |
-0.536 |
0.595 |
1 |
0.37 |
0.866 |
0.288 |
-0.302 |
| sewers |
-0.572 |
0.409 |
0.37 |
1 |
0.601 |
0.735 |
-0.686 |
| structures |
-0.498 |
0.776 |
0.866 |
0.601 |
1 |
0.469 |
-0.377 |
| shipping |
-0.485 |
0.463 |
0.288 |
0.735 |
0.469 |
1 |
-0.53 |
| fisheries |
0.456 |
-0.28 |
-0.302 |
-0.686 |
-0.377 |
-0.53 |
1 |

4.2. Univariate regressions
We used linear models for the regressions on community characteristics. Variables have been standardized by mean and standard-deviation (coefficients need to be back-transformed to be used in predictive models). Variable selection was not needed here, as we are interested in all exposure indices.
Results of regressions (coefficients with a significant p-value for marginal tests) are shown below. Using both abiotic parameters and exposure indices as predictors do not increase significantly predictive power compared to the other models. Details of the regressions, with diagnostics and cross-validation, are summarized below.
All variables
| Depth |
|
|
|
+ |
+ |
| OM |
|
|
|
|
|
| Gravel |
|
|
|
|
|
| Silt |
|
|
|
|
|
| Clay |
|
|
|
|
|
| Arsenic |
|
|
|
|
|
| Cadmium |
|
|
- |
|
|
| Chromium |
|
|
|
|
|
| Copper |
|
|
- |
|
- |
| Iron |
- |
|
|
|
|
| Manganese |
|
|
|
|
|
| Mercury |
|
|
|
|
|
| Lead |
|
|
|
|
|
| Zinc |
|
|
+ |
|
+ |
| Aquaculture |
|
|
|
|
|
| Dredging |
|
|
|
|
|
| Runoff |
|
|
|
+ |
+ |
| Sewers |
|
|
|
|
|
| Structures |
|
|
+ |
|
|
| Shipping |
+ |
|
|
|
|
| Fisheries |
+ |
|
|
|
|
| Adjusted \(R^{2}\) |
0.23 |
0.02 |
0.2 |
0.33 |
0.12 |
Richness
## Adjusted R2 is: 0.23
Fitting linear model: S ~ depth + om + gravel + silt + clay + arsenic + cadmium + chromium + copper + iron + manganese + mercury + lead + zinc + aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
| (Intercept) |
-6.409e-16 |
0.08465 |
-7.57e-15 |
1 |
|
| depth |
0.1302 |
0.1277 |
1.02 |
0.3108 |
|
| om |
0.3336 |
0.1842 |
1.811 |
0.07358 |
|
| gravel |
-0.04503 |
0.1092 |
-0.4124 |
0.6811 |
|
| silt |
-0.006452 |
0.1714 |
-0.03764 |
0.9701 |
|
| clay |
0.007711 |
0.1058 |
0.07289 |
0.9421 |
|
| arsenic |
0.07328 |
0.2072 |
0.3537 |
0.7244 |
|
| cadmium |
-0.1993 |
0.3127 |
-0.6374 |
0.5256 |
|
| chromium |
-0.2509 |
0.5132 |
-0.4889 |
0.6261 |
|
| copper |
-0.4804 |
0.8275 |
-0.5806 |
0.5631 |
|
| iron |
-0.3204 |
0.1425 |
-2.248 |
0.02712 |
* |
| manganese |
0.3417 |
0.2987 |
1.144 |
0.2557 |
|
| mercury |
-0.03731 |
0.1854 |
-0.2013 |
0.841 |
|
| lead |
-0.03209 |
0.5565 |
-0.05767 |
0.9541 |
|
| zinc |
0.3722 |
0.7441 |
0.5001 |
0.6183 |
|
| aquaculture |
0.1093 |
0.1356 |
0.806 |
0.4225 |
|
| dredging |
-0.2548 |
0.1913 |
-1.332 |
0.1864 |
|
| runoff |
0.2944 |
0.2826 |
1.042 |
0.3005 |
|
| sewers |
-0.07597 |
0.2248 |
-0.338 |
0.7362 |
|
| structures |
-0.05046 |
0.2796 |
-0.1805 |
0.8572 |
|
| shipping |
0.3021 |
0.147 |
2.056 |
0.04283 |
* |
| fisheries |
0.2629 |
0.1099 |
2.392 |
0.01892 |
* |
## RMSE from cross-validation: 1.026268
Variance Inflation Factors
| VIF |
1.5 |
2.17 |
1.28 |
2.02 |
1.24 |
2.44 |
3.68 |
6.03 |
9.73 |
1.68 |
3.51 |
2.18 |
6.54 |
8.75 |
1.59 |
2.25 |
3.32 |
2.64 |
3.29 |
1.73 |
1.29 |

Density
## Adjusted R2 is: 0.02
Fitting linear model: N ~ depth + om + gravel + silt + clay + arsenic + cadmium + chromium + copper + iron + manganese + mercury + lead + zinc + aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
| (Intercept) |
4.738e-16 |
0.0954 |
4.967e-15 |
1 |
|
| depth |
-0.23 |
0.1439 |
-1.598 |
0.1137 |
|
| om |
0.1904 |
0.2075 |
0.9173 |
0.3615 |
|
| gravel |
0.04305 |
0.123 |
0.3499 |
0.7273 |
|
| silt |
-0.1865 |
0.1932 |
-0.9654 |
0.3371 |
|
| clay |
-0.1854 |
0.1192 |
-1.555 |
0.1236 |
|
| arsenic |
0.07132 |
0.2334 |
0.3055 |
0.7607 |
|
| cadmium |
0.4086 |
0.3524 |
1.16 |
0.2494 |
|
| chromium |
-0.7084 |
0.5784 |
-1.225 |
0.224 |
|
| copper |
0.8192 |
0.9325 |
0.8785 |
0.3821 |
|
| iron |
-0.1265 |
0.1606 |
-0.7879 |
0.4329 |
|
| manganese |
0.1497 |
0.3365 |
0.4448 |
0.6576 |
|
| mercury |
-0.1331 |
0.2089 |
-0.6373 |
0.5256 |
|
| lead |
0.06011 |
0.6271 |
0.09586 |
0.9239 |
|
| zinc |
-0.806 |
0.8386 |
-0.9611 |
0.3392 |
|
| aquaculture |
-0.002814 |
0.1528 |
-0.01842 |
0.9853 |
|
| dredging |
-0.1067 |
0.2156 |
-0.4947 |
0.6221 |
|
| runoff |
-0.01846 |
0.3185 |
-0.05797 |
0.9539 |
|
| sewers |
0.2833 |
0.2533 |
1.119 |
0.2664 |
|
| structures |
-0.1471 |
0.3151 |
-0.4669 |
0.6418 |
|
| shipping |
-0.06749 |
0.1656 |
-0.4075 |
0.6846 |
|
| fisheries |
0.07969 |
0.1238 |
0.6437 |
0.5215 |
|
## RMSE from cross-validation: 1.118892
Variance Inflation Factors
| VIF |
1.5 |
2.17 |
1.28 |
2.02 |
1.24 |
2.44 |
3.68 |
6.03 |
9.73 |
1.68 |
3.51 |
2.18 |
6.54 |
8.75 |
1.59 |
2.25 |
3.32 |
2.64 |
3.29 |
1.73 |
1.29 |

Biomass
## Adjusted R2 is: 0.2
Fitting linear model: B ~ depth + om + gravel + silt + clay + arsenic + cadmium + chromium + copper + iron + manganese + mercury + lead + zinc + aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
| (Intercept) |
-1.37e-15 |
0.0863 |
-1.588e-14 |
1 |
|
| depth |
-0.2313 |
0.1302 |
-1.776 |
0.07925 |
|
| om |
0.09402 |
0.1877 |
0.5008 |
0.6178 |
|
| gravel |
0.005369 |
0.1113 |
0.04823 |
0.9616 |
|
| silt |
-0.1639 |
0.1748 |
-0.9377 |
0.351 |
|
| clay |
-0.05424 |
0.1078 |
-0.5029 |
0.6163 |
|
| arsenic |
0.3814 |
0.2112 |
1.806 |
0.07439 |
|
| cadmium |
-0.8774 |
0.3187 |
-2.753 |
0.007207 |
* * |
| chromium |
0.4618 |
0.5232 |
0.8827 |
0.3799 |
|
| copper |
-2.233 |
0.8435 |
-2.647 |
0.009659 |
* * |
| iron |
-0.1233 |
0.1453 |
-0.849 |
0.3983 |
|
| manganese |
0.01028 |
0.3044 |
0.03378 |
0.9731 |
|
| mercury |
0.3607 |
0.189 |
1.909 |
0.05961 |
|
| lead |
-0.4934 |
0.5673 |
-0.8698 |
0.3869 |
|
| zinc |
2.013 |
0.7586 |
2.654 |
0.009466 |
* * |
| aquaculture |
-0.1576 |
0.1382 |
-1.141 |
0.2572 |
|
| dredging |
-0.1426 |
0.1951 |
-0.7311 |
0.4667 |
|
| runoff |
-0.4212 |
0.2881 |
-1.462 |
0.1473 |
|
| sewers |
-0.01312 |
0.2291 |
-0.05725 |
0.9545 |
|
| structures |
0.6896 |
0.285 |
2.419 |
0.01765 |
* |
| shipping |
-0.04084 |
0.1498 |
-0.2726 |
0.7858 |
|
| fisheries |
-0.07428 |
0.112 |
-0.6632 |
0.509 |
|
## RMSE from cross-validation: 1.172694
Variance Inflation Factors
| VIF |
1.5 |
2.17 |
1.28 |
2.02 |
1.24 |
2.44 |
3.68 |
6.03 |
9.73 |
1.68 |
3.51 |
2.18 |
6.54 |
8.75 |
1.59 |
2.25 |
3.32 |
2.64 |
3.29 |
1.73 |
1.29 |

Diversity
## Adjusted R2 is: 0.33
Fitting linear model: H ~ depth + om + gravel + silt + clay + arsenic + cadmium + chromium + copper + iron + manganese + mercury + lead + zinc + aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
| (Intercept) |
-9.207e-16 |
0.07896 |
-1.166e-14 |
1 |
|
| depth |
0.4534 |
0.1191 |
3.806 |
0.000264 |
* * * |
| om |
0.295 |
0.1718 |
1.717 |
0.08952 |
|
| gravel |
-0.07467 |
0.1019 |
-0.7331 |
0.4655 |
|
| silt |
-0.01644 |
0.1599 |
-0.1028 |
0.9184 |
|
| clay |
0.14 |
0.09868 |
1.419 |
0.1596 |
|
| arsenic |
0.04943 |
0.1932 |
0.2558 |
0.7987 |
|
| cadmium |
-0.5268 |
0.2917 |
-1.806 |
0.07438 |
|
| chromium |
-0.1193 |
0.4787 |
-0.2492 |
0.8038 |
|
| copper |
-1.503 |
0.7719 |
-1.947 |
0.05479 |
|
| iron |
-0.2248 |
0.1329 |
-1.691 |
0.0945 |
|
| manganese |
0.4479 |
0.2786 |
1.608 |
0.1116 |
|
| mercury |
0.08304 |
0.1729 |
0.4802 |
0.6323 |
|
| lead |
0.1537 |
0.5191 |
0.2961 |
0.7678 |
|
| zinc |
1.343 |
0.6941 |
1.935 |
0.05628 |
|
| aquaculture |
0.1823 |
0.1265 |
1.442 |
0.1531 |
|
| dredging |
-0.1584 |
0.1785 |
-0.8877 |
0.3772 |
|
| runoff |
0.6293 |
0.2636 |
2.387 |
0.01918 |
* |
| sewers |
-0.01829 |
0.2096 |
-0.08722 |
0.9307 |
|
| structures |
-0.181 |
0.2608 |
-0.6941 |
0.4895 |
|
| shipping |
0.1713 |
0.1371 |
1.25 |
0.2147 |
|
| fisheries |
0.1266 |
0.1025 |
1.235 |
0.2202 |
|
## RMSE from cross-validation: 1.057078
Variance Inflation Factors
| VIF |
1.5 |
2.17 |
1.28 |
2.02 |
1.24 |
2.44 |
3.68 |
6.03 |
9.73 |
1.68 |
3.51 |
2.18 |
6.54 |
8.75 |
1.59 |
2.25 |
3.32 |
2.64 |
3.29 |
1.73 |
1.29 |

Evenness
## Adjusted R2 is: 0.12
Fitting linear model: J ~ depth + om + gravel + silt + clay + arsenic + cadmium + chromium + copper + iron + manganese + mercury + lead + zinc + aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
| (Intercept) |
-1.138e-15 |
0.09034 |
-1.26e-14 |
1 |
|
| depth |
0.469 |
0.1363 |
3.441 |
0.0008977 |
* * * |
| om |
-0.02793 |
0.1965 |
-0.1421 |
0.8873 |
|
| gravel |
-0.0004933 |
0.1165 |
-0.004233 |
0.9966 |
|
| silt |
0.1079 |
0.183 |
0.5899 |
0.5568 |
|
| clay |
0.2154 |
0.1129 |
1.908 |
0.05977 |
|
| arsenic |
0.02584 |
0.2211 |
0.1169 |
0.9072 |
|
| cadmium |
-0.6172 |
0.3337 |
-1.85 |
0.06779 |
|
| chromium |
0.1247 |
0.5477 |
0.2276 |
0.8205 |
|
| copper |
-1.781 |
0.8831 |
-2.017 |
0.0468 |
* |
| iron |
0.06011 |
0.1521 |
0.3952 |
0.6937 |
|
| manganese |
0.2591 |
0.3187 |
0.813 |
0.4185 |
|
| mercury |
0.1163 |
0.1978 |
0.588 |
0.5581 |
|
| lead |
0.3847 |
0.5939 |
0.6478 |
0.5189 |
|
| zinc |
1.586 |
0.7942 |
1.997 |
0.04898 |
* |
| aquaculture |
0.1135 |
0.1447 |
0.7844 |
0.435 |
|
| dredging |
-0.05131 |
0.2042 |
-0.2513 |
0.8022 |
|
| runoff |
0.6344 |
0.3016 |
2.103 |
0.03836 |
* |
| sewers |
-0.07083 |
0.2399 |
-0.2953 |
0.7685 |
|
| structures |
-0.1869 |
0.2984 |
-0.6263 |
0.5328 |
|
| shipping |
-0.0203 |
0.1568 |
-0.1294 |
0.8973 |
|
| fisheries |
-0.04536 |
0.1173 |
-0.3869 |
0.6998 |
|
## RMSE from cross-validation: 1.13629
Variance Inflation Factors
| VIF |
1.5 |
2.17 |
1.28 |
2.02 |
1.24 |
2.44 |
3.68 |
6.03 |
9.73 |
1.68 |
3.51 |
2.18 |
6.54 |
8.75 |
1.59 |
2.25 |
3.32 |
2.64 |
3.29 |
1.73 |
1.29 |

Abiotic parameters
| Depth |
+ |
- |
- |
+ |
+ |
| OM |
|
|
|
+ |
|
| Gravel |
|
|
|
|
|
| Silt |
|
|
|
|
|
| Clay |
|
|
|
|
|
| Arsenic |
|
|
+ |
|
|
| Cadmium |
|
|
- |
|
|
| Chromium |
|
|
|
|
|
| Copper |
|
|
- |
|
|
| Iron |
- |
|
|
|
|
| Manganese |
|
|
|
|
|
| Mercury |
|
|
+ |
|
|
| Lead |
|
|
|
|
|
| Zinc |
|
|
+ |
|
|
| Adjusted \(R^{2}\) |
0.18 |
0.06 |
0.18 |
0.32 |
0.12 |
Richness
## Adjusted R2 is: 0.18
Fitting linear model: S ~ depth + om + gravel + silt + clay + arsenic + cadmium + chromium + copper + iron + manganese + mercury + lead + zinc
| (Intercept) |
-6.177e-16 |
0.08689 |
-7.109e-15 |
1 |
|
| depth |
0.2315 |
0.1095 |
2.115 |
0.03713 |
* |
| om |
0.2195 |
0.174 |
1.261 |
0.2105 |
|
| gravel |
-0.04709 |
0.1087 |
-0.4334 |
0.6657 |
|
| silt |
-0.03854 |
0.1634 |
-0.2358 |
0.8141 |
|
| clay |
-0.01555 |
0.1032 |
-0.1507 |
0.8806 |
|
| arsenic |
-0.05694 |
0.1667 |
-0.3416 |
0.7334 |
|
| cadmium |
-0.1312 |
0.2652 |
-0.4948 |
0.6219 |
|
| chromium |
-0.1282 |
0.4322 |
-0.2967 |
0.7674 |
|
| copper |
-0.05041 |
0.5083 |
-0.09917 |
0.9212 |
|
| iron |
-0.3674 |
0.1236 |
-2.973 |
0.003754 |
* * |
| manganese |
0.1596 |
0.2594 |
0.6155 |
0.5397 |
|
| mercury |
0.06406 |
0.1519 |
0.4218 |
0.6742 |
|
| lead |
-0.2005 |
0.4493 |
-0.4463 |
0.6564 |
|
| zinc |
0.1353 |
0.6344 |
0.2133 |
0.8316 |
|
## RMSE from cross-validation: 1.006118
Variance Inflation Factors
| VIF |
1.25 |
1.99 |
1.24 |
1.87 |
1.18 |
1.91 |
3.04 |
4.95 |
5.82 |
1.42 |
2.97 |
1.74 |
5.15 |
7.27 |

Density
## Adjusted R2 is: 0.06
Fitting linear model: N ~ depth + om + gravel + silt + clay + arsenic + cadmium + chromium + copper + iron + manganese + mercury + lead + zinc
| (Intercept) |
5.524e-16 |
0.09348 |
5.909e-15 |
1 |
|
| depth |
-0.252 |
0.1178 |
-2.139 |
0.03505 |
* |
| om |
0.1448 |
0.1873 |
0.773 |
0.4415 |
|
| gravel |
0.05044 |
0.1169 |
0.4314 |
0.6671 |
|
| silt |
-0.1457 |
0.1758 |
-0.8289 |
0.4093 |
|
| clay |
-0.1485 |
0.1111 |
-1.338 |
0.1843 |
|
| arsenic |
0.01773 |
0.1793 |
0.09886 |
0.9215 |
|
| cadmium |
0.4549 |
0.2853 |
1.594 |
0.1142 |
|
| chromium |
-0.5273 |
0.4651 |
-1.134 |
0.2598 |
|
| copper |
0.7258 |
0.5469 |
1.327 |
0.1877 |
|
| iron |
-0.1645 |
0.133 |
-1.237 |
0.2191 |
|
| manganese |
0.05942 |
0.2791 |
0.2129 |
0.8319 |
|
| mercury |
-0.1153 |
0.1634 |
-0.7058 |
0.4821 |
|
| lead |
0.3046 |
0.4834 |
0.6302 |
0.5301 |
|
| zinc |
-0.9698 |
0.6826 |
-1.421 |
0.1587 |
|
## RMSE from cross-validation: 1.033773
Variance Inflation Factors
| VIF |
1.25 |
1.99 |
1.24 |
1.87 |
1.18 |
1.91 |
3.04 |
4.95 |
5.82 |
1.42 |
2.97 |
1.74 |
5.15 |
7.27 |

Biomass
## Adjusted R2 is: 0.18
Fitting linear model: B ~ depth + om + gravel + silt + clay + arsenic + cadmium + chromium + copper + iron + manganese + mercury + lead + zinc
| (Intercept) |
-1.012e-15 |
0.08716 |
-1.161e-14 |
1 |
|
| depth |
-0.2195 |
0.1098 |
-1.999 |
0.04855 |
* |
| om |
0.06075 |
0.1746 |
0.348 |
0.7286 |
|
| gravel |
0.01997 |
0.109 |
0.1832 |
0.855 |
|
| silt |
-0.1704 |
0.1639 |
-1.039 |
0.3013 |
|
| clay |
-0.04205 |
0.1035 |
-0.4061 |
0.6856 |
|
| arsenic |
0.3541 |
0.1672 |
2.118 |
0.03685 |
* |
| cadmium |
-0.6433 |
0.266 |
-2.419 |
0.01753 |
* |
| chromium |
0.4378 |
0.4336 |
1.01 |
0.3153 |
|
| copper |
-1.556 |
0.5099 |
-3.051 |
0.00297 |
* * |
| iron |
-0.01015 |
0.124 |
-0.08185 |
0.9349 |
|
| manganese |
0.07822 |
0.2602 |
0.3006 |
0.7644 |
|
| mercury |
0.3488 |
0.1524 |
2.289 |
0.02433 |
* |
| lead |
-0.8704 |
0.4507 |
-1.931 |
0.05648 |
|
| zinc |
1.625 |
0.6364 |
2.553 |
0.01229 |
* |
## RMSE from cross-validation: 1.179323
Variance Inflation Factors
| VIF |
1.25 |
1.99 |
1.24 |
1.87 |
1.18 |
1.91 |
3.04 |
4.95 |
5.82 |
1.42 |
2.97 |
1.74 |
5.15 |
7.27 |

Diversity
## Adjusted R2 is: 0.32
Fitting linear model: H ~ depth + om + gravel + silt + clay + arsenic + cadmium + chromium + copper + iron + manganese + mercury + lead + zinc
| (Intercept) |
-6.949e-16 |
0.07917 |
-8.777e-15 |
1 |
|
| depth |
0.4211 |
0.09976 |
4.222 |
5.643e-05 |
* * * |
| om |
0.3487 |
0.1586 |
2.199 |
0.03038 |
* |
| gravel |
-0.1207 |
0.099 |
-1.219 |
0.2258 |
|
| silt |
-0.1186 |
0.1489 |
-0.7969 |
0.4276 |
|
| clay |
0.08567 |
0.09404 |
0.911 |
0.3647 |
|
| arsenic |
-0.08548 |
0.1519 |
-0.5628 |
0.5749 |
|
| cadmium |
-0.2729 |
0.2416 |
-1.13 |
0.2615 |
|
| chromium |
-0.4294 |
0.3938 |
-1.09 |
0.2784 |
|
| copper |
-0.3445 |
0.4632 |
-0.7437 |
0.4589 |
|
| iron |
-0.2159 |
0.1126 |
-1.918 |
0.05819 |
|
| manganese |
0.2222 |
0.2363 |
0.94 |
0.3497 |
|
| mercury |
0.1032 |
0.1384 |
0.7456 |
0.4578 |
|
| lead |
-0.1383 |
0.4093 |
-0.3379 |
0.7362 |
|
| zinc |
0.749 |
0.5781 |
1.296 |
0.1983 |
|
## RMSE from cross-validation: 0.9988091
Variance Inflation Factors
| VIF |
1.25 |
1.99 |
1.24 |
1.87 |
1.18 |
1.91 |
3.04 |
4.95 |
5.82 |
1.42 |
2.97 |
1.74 |
5.15 |
7.27 |

Evenness
## Adjusted R2 is: 0.12
Fitting linear model: J ~ depth + om + gravel + silt + clay + arsenic + cadmium + chromium + copper + iron + manganese + mercury + lead + zinc
| (Intercept) |
-9.229e-16 |
0.09028 |
-1.022e-14 |
1 |
|
| depth |
0.3458 |
0.1138 |
3.04 |
0.003077 |
* * |
| om |
0.1431 |
0.1809 |
0.791 |
0.431 |
|
| gravel |
-0.06604 |
0.1129 |
-0.5849 |
0.56 |
|
| silt |
-0.02428 |
0.1698 |
-0.143 |
0.8866 |
|
| clay |
0.1515 |
0.1072 |
1.413 |
0.161 |
|
| arsenic |
-0.08341 |
0.1732 |
-0.4816 |
0.6312 |
|
| cadmium |
-0.3626 |
0.2755 |
-1.316 |
0.1914 |
|
| chromium |
-0.3113 |
0.4491 |
-0.693 |
0.49 |
|
| copper |
-0.6336 |
0.5282 |
-1.2 |
0.2333 |
|
| iron |
0.08102 |
0.1284 |
0.631 |
0.5296 |
|
| manganese |
0.07169 |
0.2695 |
0.266 |
0.7908 |
|
| mercury |
0.03764 |
0.1578 |
0.2385 |
0.8121 |
|
| lead |
0.1069 |
0.4668 |
0.2289 |
0.8194 |
|
| zinc |
1.026 |
0.6592 |
1.556 |
0.1232 |
|
## RMSE from cross-validation: 1.061198
Variance Inflation Factors
| VIF |
1.25 |
1.99 |
1.24 |
1.87 |
1.18 |
1.91 |
3.04 |
4.95 |
5.82 |
1.42 |
2.97 |
1.74 |
5.15 |
7.27 |

Exposure indices
| Depth |
|
|
|
+ |
+ |
| Aquaculture |
|
|
|
|
|
| Dredging |
|
|
|
|
|
| Runoff |
|
|
- |
+ |
|
| Sewers |
- |
|
- |
|
|
| Structures |
|
|
+ |
|
|
| Shipping |
+ |
|
|
|
|
| Fisheries |
+ |
|
|
|
|
| Adjusted \(R^{2}\) |
0.16 |
0.02 |
0.04 |
0.29 |
0.14 |
Richness
## Adjusted R2 is: 0.16
Fitting linear model: S ~ aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
| (Intercept) |
-2.208e-16 |
0.0882 |
-2.504e-15 |
1 |
|
| aquaculture |
0.1387 |
0.1246 |
1.114 |
0.2681 |
|
| dredging |
-0.1659 |
0.1192 |
-1.392 |
0.167 |
|
| runoff |
0.1802 |
0.2009 |
0.8969 |
0.3719 |
|
| sewers |
-0.3168 |
0.1586 |
-1.998 |
0.04846 |
* |
| structures |
-0.05714 |
0.2315 |
-0.2468 |
0.8056 |
|
| shipping |
0.3368 |
0.1164 |
2.894 |
0.004668 |
* * |
| fisheries |
0.2269 |
0.106 |
2.141 |
0.03474 |
* |
## RMSE from cross-validation: 0.945198
Variance Inflation Factors
| VIF |
1.41 |
1.34 |
2.27 |
1.79 |
2.61 |
1.31 |
1.2 |

Density
## Adjusted R2 is: 0.02
Fitting linear model: N ~ depth + aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
| (Intercept) |
1.925e-16 |
0.09539 |
2.018e-15 |
1 |
|
| depth |
-0.185 |
0.1122 |
-1.649 |
0.1024 |
|
| aquaculture |
0.05215 |
0.1347 |
0.3871 |
0.6995 |
|
| dredging |
-0.1216 |
0.129 |
-0.9424 |
0.3483 |
|
| runoff |
0.1879 |
0.2201 |
0.854 |
0.3952 |
|
| sewers |
0.1503 |
0.1855 |
0.8101 |
0.4198 |
|
| structures |
-0.1768 |
0.2539 |
-0.6963 |
0.4879 |
|
| shipping |
-0.09248 |
0.133 |
-0.6955 |
0.4884 |
|
| fisheries |
0.122 |
0.1151 |
1.06 |
0.2918 |
|
## RMSE from cross-validation: 1.06337
Variance Inflation Factors
| VIF |
1.17 |
1.41 |
1.35 |
2.3 |
1.94 |
2.65 |
1.39 |
1.2 |

Biomass
## Adjusted R2 is: 0.04
Fitting linear model: B ~ depth + aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
| (Intercept) |
-6.466e-17 |
0.09425 |
-6.861e-16 |
1 |
|
| depth |
-0.2062 |
0.1109 |
-1.86 |
0.06585 |
|
| aquaculture |
-0.2534 |
0.1331 |
-1.904 |
0.05983 |
|
| dredging |
-0.009733 |
0.1275 |
-0.07637 |
0.9393 |
|
| runoff |
-0.4785 |
0.2174 |
-2.201 |
0.03008 |
* |
| sewers |
-0.5772 |
0.1833 |
-3.149 |
0.002167 |
* * |
| structures |
0.5394 |
0.2509 |
2.15 |
0.03399 |
* |
| shipping |
0.09399 |
0.1314 |
0.7154 |
0.4761 |
|
| fisheries |
-0.09747 |
0.1138 |
-0.8568 |
0.3936 |
|
## RMSE from cross-validation: 1.018014
Variance Inflation Factors
| VIF |
1.17 |
1.41 |
1.35 |
2.3 |
1.94 |
2.65 |
1.39 |
1.2 |

Diversity
## Adjusted R2 is: 0.29
Fitting linear model: H ~ depth + aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
| (Intercept) |
-1.513e-16 |
0.08128 |
-1.861e-15 |
1 |
|
| depth |
0.535 |
0.09561 |
5.596 |
1.956e-07 |
* * * |
| aquaculture |
0.1666 |
0.1148 |
1.451 |
0.1499 |
|
| dredging |
0.005753 |
0.1099 |
0.05234 |
0.9584 |
|
| runoff |
0.4163 |
0.1875 |
2.22 |
0.02868 |
* |
| sewers |
0.002447 |
0.1581 |
0.01548 |
0.9877 |
|
| structures |
-0.3441 |
0.2163 |
-1.59 |
0.115 |
|
| shipping |
0.1112 |
0.1133 |
0.9812 |
0.3289 |
|
| fisheries |
0.02589 |
0.0981 |
0.2639 |
0.7924 |
|
## RMSE from cross-validation: 0.889925
Variance Inflation Factors
| VIF |
1.17 |
1.41 |
1.35 |
2.3 |
1.94 |
2.65 |
1.39 |
1.2 |

Evenness
## Adjusted R2 is: 0.14
Fitting linear model: J ~ depth + aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
| (Intercept) |
-5.164e-16 |
0.08921 |
-5.789e-15 |
1 |
|
| depth |
0.4573 |
0.1049 |
4.358 |
3.213e-05 |
* * * |
| aquaculture |
0.06496 |
0.126 |
0.5156 |
0.6073 |
|
| dredging |
0.1359 |
0.1206 |
1.126 |
0.2627 |
|
| runoff |
0.3196 |
0.2058 |
1.553 |
0.1236 |
|
| sewers |
0.06674 |
0.1735 |
0.3846 |
0.7013 |
|
| structures |
-0.3271 |
0.2375 |
-1.377 |
0.1715 |
|
| shipping |
-0.04701 |
0.1244 |
-0.3779 |
0.7063 |
|
| fisheries |
-0.1252 |
0.1077 |
-1.163 |
0.2478 |
|
## RMSE from cross-validation: 0.986413
Variance Inflation Factors
| VIF |
1.17 |
1.41 |
1.35 |
2.3 |
1.94 |
2.65 |
1.39 |
1.2 |

4.3. Multivariate regression
The model selected by the DistLM procedure has a \(R^{2}\) of 0.22 (results extracted form PRIMER-e). Stations are colored according to the cumulative exposure index (cf. Marine Strategy Framework Directive):
- indigo = lowest exposure ($E_{ij} < 0.2) ~ high status
- green = low exposure (0.2 ≤ $E_{ij} < 0.4) ~ good status
- yellow = moderate exposure (0.4 ≤ $E_{ij} < 0.6) ~ moderate status
- orange = high exposure (0.6 ≤ $E_{ij} < 0.8) ~ poor status
- crimson = highest exposure ($E_{ij} ≥ 0.8) ~ bad status


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